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International Conference on Image Processing

作者： Di YANG Hongdong LI RSISE Australian National University

ISBN: (纸本)9781424456536;9781424456543

demons algorithm has attracted considerable attention from the image processing community for registering (i.e., matching/aligning) deformable objects or images. It is observed that this algorithm is particularly successful when it applies to nonrigid object having homogenous region, but often fails when the object of interest is rich in texture. This is mainly because the demons algorithm tends to overfit the many spurious edges inside the texture-rich region, consequently leading to erroneous thermodynamic 'forces'. In this paper, we describe a probabilistic demons algorithm that overcomes this problem. Our key idea is to re-formulate the deformable registration problem in Bayesian statistics framework. The result is a new and more robust demons algorithm able to capture the essence (e.g., the mass) of a deformable image/object even it is rich in texture. This will significantly expand the applicable scopes of the traditional demons algorithm. We give encouraging experimental results on real test images.

关键词： Image Registration demons algorithm Model-based fitting image registration algorithms mode matching wealth, wealthy texture Essences textures images

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Deformable registration of the inflated and deflated lung in cone-beam CT-guided thoracic surgery: Initial investigation of a combined model- and image-driven approach

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MEDICAL PHYSICS 2013年第1期40卷 017501-017501页

作者： Uneri, Ali Nithiananthan, Sajendra Schafer, Sebastian Otake, Yoshito Stayman, J. Webster Kleinszig, Gerhard Sussman, Marc S. Prince, Jerry L. Siewerdsen, Jeffrey H. Johns Hopkins Univ Dept Biomed Engn Baltimore MD 21205 USA Johns Hopkins Univ Dept Comp Sci Baltimore MD 21218 USA Siemens Healthcare D-91052 Erlangen Germany Johns Hopkins Univ Dept Surg Baltimore MD 21224 USA Johns Hopkins Univ Dept Elect & Comp Engn Baltimore MD 21218 USA

Purpose: Surgical resection is the preferred modality for curative treatment of early stage lung cancer, but localization of small tumors (<10 mm diameter) during surgery presents a major challenge that is likely to increase as more early-stage disease is detected incidentally and in low-dose CT screening. To overcome the difficulty of manual localization (fingers inserted through intercostal ports) and the cost, logistics, and morbidity of preoperative tagging (coil or dye placement under CT-fluoroscopy), the authors propose the use of intraoperative cone-beam CT (CBCT) and deformable image registration to guide targeting of small tumors in video-assisted thoracic surgery (VATS). A novel algorithm is reported for registration of the lung from its inflated state (prior to pleural breach) to the deflated state (during resection) to localize surgical targets and adjacent critical anatomy. Methods: The registration approach geometrically resolves images of the inflated and deflated lung using a coarse model-driven stage followed by a finer image-driven stage. The model-driven stage uses image features derived from the lung surfaces and airways: triangular surface meshes are morphed to capture bulk motion;concurrently, the airways generate graph structures from which corresponding nodes are identified. Interpolation of the sparse motion fields computed from the bounding surface and interior airways provides a 3D motion field that coarsely registers the lung and initializes the subsequent image-driven stage. The image-driven stage employs an intensity-corrected, symmetric form of the demons method. The algorithm was validated over 12 datasets, obtained from porcine specimen experiments emulating CBCT-guided VATS. Geometric accuracy was quantified in terms of target registration error (TRE) in anatomical targets throughout the lung, and normalized cross-correlation. Variations of the algorithm were investigated to study the behavior of the model-and image-driven stages by

关键词： deformable image registration image-guided surgery cone-beam CT thoracic surgery demons algorithm intraoperative imaging

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Template-based automatic breast segmentation on MRI by excluding the chest region

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MEDICAL PHYSICS 2013年第12期40卷 122301-122301页

作者： Lin, Muqing Chen, Jeon-Hor Wang, Xiaoyong Chan, Siwa Chen, Siping Su, Min-Ying Univ Calif Irvine Dept Radiol Sci Tu & Yuen Ctr Funct Oncoimaging Irvine CA 92697 USA Shenzhen Univ Natl Reg Key Technol Engn Lab Med Ultrasound Guangdong Key Lab Biomed Measurements & Ultrasoun Dept Biomed EngnSch Med Shenzhen 518060 Peoples R China E Da Hosp Dept Radiol Kaohsiung 82445 Taiwan I Shou Univ Kaohsiung 82445 Taiwan Taichung Vet Gen Hosp Dept Radiol Taichung 40407 Taiwan

Purpose: Methods for quantification of breast density on MRI using semiautomatic approaches are commonly used. In this study, the authors report on a fully automatic chest template-based method. Methods: Nonfat-suppressed breast MR images from 31 healthy women were analyzed. Among them, one case was randomly selected and used as the template, and the remaining 30 cases were used for testing. Unlike most model-based breast segmentation methods that use the breast region as the template, the chest body region on a middle slice was used as the template. Within the chest template, three body landmarks (thoracic spine and bilateral boundary of the pectoral muscle) were identified for performing the initial V-shape cut to determine the posterior lateral boundary of the breast. The chest template was mapped to each subject's image space to obtain a subject-specific chest model for exclusion. On the remaining image, the chest wall muscle was identified and excluded to obtain clean breast segmentation. The chest and muscle boundaries determined on the middle slice were used as the reference for the segmentation of adjacent slices, and the process continued superiorly and inferiorly until all 3D slices were segmented. The segmentation results were evaluated by an experienced radiologist to mark voxels that were wrongly included or excluded for error analysis. Results: The breast volumes measured by the proposed algorithm were very close to the radiologist's corrected volumes, showing a % difference ranging from 0.01% to 3.04% in 30 tested subjects with a mean of 0.86% +/- 0.72%. The total error was calculated by adding the inclusion and the exclusion errors (so they did not cancel each other out), which ranged from 0.05% to 6.75% with a mean of 3.05% +/- 1.93%. The fibroglandular tissue segmented within the breast region determined by the algorithm and the radiologist were also very close, showing a % difference ranging from 0.02% to 2.52% with a mean of 1.03% +/- 1.03%. The

关键词： model-based breast segmentation chest body template breast MRI breast density demons algorithm

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iLogdemons: A demons-Based Registration algorithm for Tracking Incompressible Elastic Biological Tissues

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INTERNATIONAL JOURNAL OF COMPUTER VISION 2011年第1期92卷 92-111页

作者： Mansi, Tommaso Pennec, Xavier Sermesant, Maxime Delingette, Herve Ayache, Nicholas INRIA Sophia Antipolis Mediterranee Asclepios Res Project F-06902 Sophia Antipolis France

Tracking soft tissues in medical images using non-linear image registration algorithms requires methods that are fast and provide spatial transformations consistent with the biological characteristics of the tissues. Logdemons algorithm is a fast non-linear registration method that computes diffeomorphic transformations parameterised by stationary velocity fields. Although computationally efficient, its use for tissue tracking has been limited because of its ad-hoc Gaussian regularisation, which hampers the implementation of more biologically motivated regularisations. In this work, we improve the logdemons by integrating elasticity and incompressibility for soft-tissue tracking. To that end, a mathematical justification of demons Gaussian regularisation is proposed. Building on this result, we replace the Gaussian smoothing by an efficient elastic-like regulariser based on isotropic differential quadratic forms of vector fields. The registration energy functional is finally minimised under the divergence-free constraint to get incompressible deformations. As the elastic regulariser and the constraint are linear, the method remains computationally tractable and easy to implement. Tests on synthetic incompressible deformations showed that our approach outperforms the original logdemons in terms of elastic incompressible deformation recovery without reducing the image matching accuracy. As an application, we applied the proposed algorithm to estimate 3D myocardium strain on clinical cine MRI of two adult patients. Results showed that incompressibility constraint improves the cardiac motion recovery when compared to the ground truth provided by 3D tagged MRI.

关键词： Non-linear image registration demons algorithm Elastic-like regularisation Incompressible registration Divergence-free stationary velocity field Cardiac motion recovery

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Extra-dimensional demons: A method for incorporating missing tissue in deformable image registration

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MEDICAL PHYSICS 2012年第9期39卷 5718-5731页

作者： Nithiananthan, Sajendra Schafer, Sebastian Mirota, Daniel J. Stayman, J. Webster Zbijewski, Wojciech Reh, Douglas D. Gallia, Gary L. Siewerdsen, Jeffrey H. Johns Hopkins Univ Dept Biomed Engn Baltimore MD 21205 USA Johns Hopkins Univ Dept Comp Sci Baltimore MD 21218 USA Johns Hopkins Univ Dept Neurosurg Baltimore MD 21287 USA Johns Hopkins Univ Dept Otolaryngol Head & Neck Surg Baltimore MD 21287 USA

Purpose: A deformable registration method capable of accounting for missing tissue (e.g., excision) is reported for application in cone-beam CT (CBCT)-guided surgical procedures. Excisions are identified by a segmentation step performed simultaneous to the registration process. Tissue excision is explicitly modeled by increasing the dimensionality of the deformation field to allow motion beyond the dimensionality of the image. The accuracy of the model is tested in phantom, simulations, and cadaver models. Methods: A variant of the demons deformable registration algorithm is modified to include excision segmentation and modeling. Segmentation is performed iteratively during the registration process, with initial implementation using a threshold-based approach to identify voxels corresponding to "tissue" in the moving image and "air" in the fixed image. With each iteration of the demons process, every voxel is assigned a probability of excision. Excisions are modeled explicitly during registration by increasing the dimensionality of the deformation field so that both deformations and excisions can be accounted for by in- and out-of-volume deformations, respectively. The out-of-volume (i.e., fourth) component of the deformation field at each voxel carries a magnitude proportional to the excision probability computed in the excision segmentation step. The registration accuracy of the proposed "extra-dimensional" demons (XDD) and conventional demons methods was tested in the presence of missing tissue in phantom models, simulations investigating the effect of excision size on registration accuracy, and cadaver studies emulating realistic deformations and tissue excisions imparted in CBCT-guided endoscopic skull base surgery. Results: Phantom experiments showed the normalized mutual information (NMI) in regions local to the excision to improve from 1.10 for the conventional demons approach to 1.16 for XDD, and qualitative examination of the resulting images revealed majo

关键词： deformable image registration demons algorithm segmentation cone-beam CT image-guided surgery

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demons deformable registration of CT and cone-beam CT using an iterative intensity matching approach

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MEDICAL PHYSICS 2011年第4期38卷 1785-1798页

作者： Nithiananthan, Sajendra Schafer, Sebastian Uneri, Ali Mirota, Daniel J. Stayman, J. Webster Zbijewski, Wojciech Brock, Kristy K. Daly, Michael J. Chan, Harley Irish, Jonathan C. Siewerdsen, Jeffrey H. Johns Hopkins Univ Dept Biomed Engn Baltimore MD 21205 USA Johns Hopkins Univ Dept Comp Sci Baltimore MD 21205 USA Johns Hopkins Univ Dept Radiol Baltimore MD 21205 USA Princess Margaret Hosp Ontario Canc Inst Toronto ON M5G 2M9 Canada Univ Toronto Dept Otolaryngol Head & Neck Surg Toronto ON M5G 2M9 Canada

Purpose: A method of intensity-based deformable registration of CT and cone-beam CT (CBCT) images is described, in which intensity correction occurs simultaneously within the iterative registration process. The method preserves the speed and simplicity of the popular demons algorithm while providing robustness and accuracy in the presence of large mismatch between CT and CBCT voxel values ("intensity"). Methods: A variant of the demons algorithm was developed in which an estimate of the relationship between CT and CBCT intensity values for specific materials in the image is computed at each iteration based on the set of currently overlapping voxels. This tissue-specific intensity correction is then used to estimate the registration output for that iteration and the process is repeated. The robustness of the method was tested in CBCT images of a cadaveric head exhibiting a broad range of simulated intensity variations associated with x-ray scatter, object truncation, and/or errors in the reconstruction algorithm. The accuracy of CT-CBCT registration was also measured in six real cases, exhibiting deformations ranging from simple to complex during surgery or radiotherapy guided by a CBCT-capable C-arm or linear accelerator, respectively. Results: The iterative intensity matching approach was robust against all levels of intensity variation examined, including spatially varying errors in voxel value of a factor of 2 or more, as can be encountered in cases of high x-ray scatter. Registration accuracy without intensity matching degraded severely with increasing magnitude of intensity error and introduced image distortion. A single histogram match performed prior to registration alleviated some of these effects but was also prone to image distortion and was quantifiably less robust and accurate than the iterative approach. Within the six case registration accuracy study, iterative intensity matching demons reduced mean TRE to (2.5 +/- 2.8) mm compared to (3.5 +/- 3.0) mm

关键词： deformable registration demons algorithm cone-beam CT computed tomography image-guided interventions

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Joint T1 and Brain Fiber Diffeomorphic Registration Using the demons

Joint T1 and Brain Fiber Diffeomorphic Registration Using th...

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1st International Workshop on Multimodal Brain Image Analysis (MBIA 2011)

作者： Siless, Viviana Guevara, Pamela Pennec, Xavier Fillard, Pierre INRIA Saclay Ile De France Parietal Team Saclay France CEA DSV I2BM Suresnes France Univ Concepcion Concepcion Chile INRIA Sophia Antipolis Mediterranee Asclepios Team Sophia Antipolis France

ISBN: (纸本)9783642244452

Non-linear image registration is one of the most challenging task in medical image analysis. In this work, we propose an extension of the well-established diffeomorphic demons registration algorithm to take into account geometric constraints. Combining the deformation field induced by the image and the geometry, we define a mathematically sound framework to jointly register images and geometric descriptors such as fibers or sulcal lines. We demonstrate this framework by registering simultaneously T-1 images and 50 fiber bundles consistently extracted in 12 subjects. Results show the improvement of fibers alignment while maintaining, and sometimes improving image registration. Further comparisons with non-linear T-1 and tensor registration demonstrate the superiority of the Geometric demons over their purely iconic counterparts.

关键词： Registration neural fibers diffeomorphism demons algorithm intensity-base registration tensor-base registration

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Pulmonary nodule registration: Rigid or nonrigid?

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MEDICAL PHYSICS 2011年第7期38卷 4406-4414页

作者： Gu, Suicheng Wilson, David Tan, Jun Pu, Jiantao Univ Pittsburgh Dept Radiol Pittsburgh PA 15213 USA Univ Pittsburgh Dept Med Pittsburgh PA 15213 USA Univ Pittsburgh Dept Bioengn Pittsburgh PA 15213 USA

Purpose: The primary aim of this study is to investigate the performance difference of rigid and nonrigid registration schemes in matching corresponding pulmonary nodules depicted on sequential chest computed tomography (CT) examinations. Methods: A gradient descent based rigid registration algorithm with scaling was developed and it handled the involved geometric transformations (i.e., translation, rescaling, shearing, and rotation) separately instead of optimizing them in a single pass. Given two lung CT examinations, the scaling and translation parameters were simply estimated from the lung volume dimensions (e.g., size and mass center), while the rotation parameters were optimized progressively using gradient descent. To investigate the performance difference of rigid and nonrigid schemes in pulmonary nodule registration, the well-known nonrigid demons algorithm was implemented and tested along with the developed schemes against 60 diverse low-dose clinical lung CT examinations with average 2-yr follow-up scans. A verified cancer and its correspondence in the follow-up scan as well as their spatial locations (mass center) were identified in each examination. In addition to the computational efficiency, the accuracy of these registration procedures was assessed by computing the Euclidean distances between the corresponding nodules after the registration. To demonstrate the advantage of the developed algorithm, the authors also implemented a fast iterative closest point (ICP) based rigid algorithm and compared their performance. Results: Our experiments on the collected chest CT examinations showed that the nodule registration errors in 3D Euclidean distance for the developed rigid affine approach, the traditional ICP algorithm, and the refining nonrigid demons algorithm were 9.6, 9.8, and 10.0 mm, respectively, and the corresponding computational costs in time were 5, 300, and 55 s, respectively. Conclusions: A rigid solution may be preferred in practice for the

关键词： pulmonary nodule rigid/nonrigid registration demons algorithm follow-up study

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A non-rigid registration method for serial lower extremity hybrid SPECT/CT imaging

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MEDICAL IMAGE ANALYSIS 2011年第1期15卷 96-111页

作者： Suh, Jung W. Scheinost, Dustin Dione, Donald P. Dobrucki, Lawrence W. Sinusas, Albert J. Papademetris, Xenophon Yale Univ Sch Med Dept Diagnost Radiol New Haven CT 06520 USA Yale Univ Dept Biomed Engn New Haven CT 06520 USA Yale Univ Dept Med New Haven CT 06520 USA

Small animal X-ray computed tomographic (microCT) imaging of the lower extremities permits evaluation of arterial growth in models of hindlimb ischemia, and when applied serially can provide quantitative information about disease progression and aid in the evaluation of therapeutic interventions. The quantification of changes in tissue perfusion and concentration of molecular markers concurrently obtained using nuclear imaging requires the ability to non-rigidly register the microCT images over time, a task made more challenging by the potentially large changes in the positions of the legs due to articulation. While non-rigid registration methods have been extensively used in the evaluation of individual organs, application in whole body imaging has been limited, primarily because the scale of possible displacements and deformations is large resulting in poor convergence of most methods. In this paper we present a new method based on the extended demons algorithm that uses a level-set representation of the body contour and skeletal structure as an input. The proposed serial registration method reflects the natural physical moving combination of mouse anatomy in which the movement of bones is the framework for body movements, and the movement of skin constrains the detailed movements of the specific segmented body regions. We applied our method to both the registration of serial microCT mouse images and the quantification of microSPECT component of the serially hybrid microCT-SPECT images demonstrating improved performance as compared to existing registration techniques. (C) 2010 Elsevier B.V. All rights reserved.

关键词： Non-rigid registration Serial mouse registration demons algorithm Large displacement

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Non-rigid mammogram registration using demons algorithm: Preliminary results

Non-rigid mammogram registration using demons algorithm: Pre...

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9th IASTED International Conference on Signal and Image Processing

作者： Guo, Yujun Cheng, Wei-Hung Lu, Cheng-Chang Kent State Univ Dept Comp Sci Kent OH 44242 USA

ISBN: (纸本)9780889866751

X-ray mammography is the main tool used for the detection and diagnosis of breast malignancies. Mammogram registration plays an important role in breast cancer detection. The inhomogeneous, anisotropic nature of the soft-tissue within the breast, its inherent non-rigid body behavior, temporal changes of the breast tissue, and various imaging conditions, make breast image registration a challenging task. In this paper, we present a novel non-rigid mammogram registration approach using demons algorithm. The algorithm is fully automatic, intensity-based, and is implemented in a multi-resolution framework. An extension is also applied to accelerate the convergence. Mammogram data sets from MIAS database are used in our study. Preliminary results are presented. The performance of the algorithm was evaluated both visually and quantitatively, and the method yields promising results.

关键词： mammography non-rigid registration bilateral registration demons algorithm

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